[SPARK-56171][SQL] Enable V2 file write path for non-partitioned DataFrame API writes and delete FallBackFileSourceV2

connector/avro/src/main/scala/org/apache/spark/sql/v2/avro/AvroTable.scala

@@ -22,7 +22,7 @@ import org.apache.hadoop.fs.FileStatus
 
 import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.avro.AvroUtils
-import org.apache.spark.sql.connector.write.{LogicalWriteInfo, Write, WriteBuilder}
+import org.apache.spark.sql.connector.write.{LogicalWriteInfo, WriteBuilder}
 import org.apache.spark.sql.execution.datasources.FileFormat
 import org.apache.spark.sql.execution.datasources.v2.FileTable
 import org.apache.spark.sql.types.{DataType, StructType}
@@ -43,13 +43,14 @@ case class AvroTable(
     AvroUtils.inferSchema(sparkSession, options.asScala.toMap, files)
 
   override def newWriteBuilder(info: LogicalWriteInfo): WriteBuilder = {
-    new WriteBuilder {
-      override def build(): Write =
-        AvroWrite(paths, formatName, supportsDataType, mergedWriteInfo(info))
+    createFileWriteBuilder(info) {
+      (mergedInfo, partSchema, customLocs, dynamicOverwrite, truncate) =>
+      AvroWrite(paths, formatName, supportsDataType, mergedInfo, partSchema, customLocs,
+        dynamicOverwrite, truncate)
     }
   }
 
   override def supportsDataType(dataType: DataType): Boolean = AvroUtils.supportsDataType(dataType)
 
-  override def formatName: String = "AVRO"
+  override def formatName: String = "Avro"
 }

connector/avro/src/main/scala/org/apache/spark/sql/v2/avro/AvroWrite.scala

@@ -29,7 +29,11 @@ case class AvroWrite(
     paths: Seq[String],
     formatName: String,
     supportsDataType: DataType => Boolean,
-    info: LogicalWriteInfo) extends FileWrite {
+    info: LogicalWriteInfo,
+    partitionSchema: StructType,
+    override val customPartitionLocations: Map[Map[String, String], String] = Map.empty,
+    override val dynamicPartitionOverwrite: Boolean,
+    override val isTruncate: Boolean) extends FileWrite {
   override def prepareWrite(
       sqlConf: SQLConf,
       job: Job,

sql/core/src/main/scala/org/apache/spark/sql/classic/DataFrameWriter.scala

@@ -194,10 +194,17 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) extends sql.DataFram
           if (curmode == SaveMode.Append) {
             AppendData.byName(relation, df.logicalPlan, finalOptions)
           } else {
-            // Truncate the table. TableCapabilityCheck will throw a nice exception if this
-            // isn't supported
-            OverwriteByExpression.byName(
-              relation, df.logicalPlan, Literal(true), finalOptions)
+            val dynamicOverwrite =
+              df.sparkSession.sessionState.conf.partitionOverwriteMode ==
+                PartitionOverwriteMode.DYNAMIC &&
+              partitioningColumns.exists(_.nonEmpty)
+            if (dynamicOverwrite) {
+              OverwritePartitionsDynamic.byName(
+                relation, df.logicalPlan, finalOptions)
+            } else {
+              OverwriteByExpression.byName(
+                relation, df.logicalPlan, Literal(true), finalOptions)
+            }
           }
 
         case createMode =>
@@ -318,7 +325,13 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) extends sql.DataFram
     }
 
     val session = df.sparkSession
-    val canUseV2 = lookupV2Provider().isDefined
+    // TODO(SPARK-56175): File source V2 does not support
+    // insertInto for catalog tables yet.
+    val canUseV2 = lookupV2Provider() match {
+      case Some(_: FileDataSourceV2) => false
+      case Some(_) => true
+      case None => false
+    }
 
     session.sessionState.sqlParser.parseMultipartIdentifier(tableName) match {
       case NonSessionCatalogAndIdentifier(catalog, ident) =>
@@ -438,9 +451,16 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) extends sql.DataFram
     import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
 
     val session = df.sparkSession
-    val v2ProviderOpt = lookupV2Provider()
-    val canUseV2 = v2ProviderOpt.isDefined || (hasCustomSessionCatalog &&
-        !df.sparkSession.sessionState.catalogManager.catalog(CatalogManager.SESSION_CATALOG_NAME)
+    // TODO(SPARK-56230): File source V2 does not support
+    // saveAsTable yet. Always use V1 for file sources.
+    val v2ProviderOpt = lookupV2Provider().flatMap {
+      case _: FileDataSourceV2 => None
+      case other => Some(other)
+    }
+    val canUseV2 = v2ProviderOpt.isDefined ||
+      (hasCustomSessionCatalog &&
+        !df.sparkSession.sessionState.catalogManager
+          .catalog(CatalogManager.SESSION_CATALOG_NAME)
           .isInstanceOf[CatalogExtension])
 
     session.sessionState.sqlParser.parseMultipartIdentifier(tableName) match {
@@ -595,8 +615,16 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) extends sql.DataFram
 
   private def lookupV2Provider(): Option[TableProvider] = {
     DataSource.lookupDataSourceV2(source, df.sparkSession.sessionState.conf) match {
-      // TODO(SPARK-28396): File source v2 write path is currently broken.
-      case Some(_: FileDataSourceV2) => None
+      // File source V2 supports non-partitioned Append and
+      // Overwrite via DataFrame API (df.write.save(path)).
+      // Fall back to V1 for:
+      // - ErrorIfExists/Ignore (TODO: SPARK-56174)
+      // - Partitioned writes (TODO: SPARK-56174)
+      case Some(_: FileDataSourceV2)
+          if (curmode != SaveMode.Append
+            && curmode != SaveMode.Overwrite)
+            || partitioningColumns.exists(_.nonEmpty) =>
+        None
       case other => other
     }
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatDataWriter.scala

@@ -17,6 +17,7 @@
 package org.apache.spark.sql.execution.datasources
 
 import scala.collection.mutable
+import scala.jdk.CollectionConverters._
 
 import org.apache.hadoop.fs.{FileAlreadyExistsException, Path}
 import org.apache.hadoop.mapreduce.TaskAttemptContext
@@ -104,6 +105,14 @@ abstract class FileFormatDataWriter(
     }
   }
 
+  /**
+   * Override writeAll to ensure V2 DataWriter.writeAll path also wraps
+   * errors with TASK_WRITE_FAILED, matching V1 behavior.
+   */
+  override def writeAll(records: java.util.Iterator[InternalRow]): Unit = {
+    writeWithIterator(records.asScala)
+  }
+
   /** Write an iterator of records. */
   def writeWithIterator(iterator: Iterator[InternalRow]): Unit = {
     var count = 0L

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Strategy.scala

@@ -70,7 +70,15 @@ class DataSourceV2Strategy(session: SparkSession) extends Strategy with Predicat
       val nameParts = ident.toQualifiedNameParts(catalog)
       cacheManager.recacheTableOrView(session, nameParts, includeTimeTravel = false)
     case _ =>
-      cacheManager.recacheByPlan(session, r)
+      r.table match {
+        case ft: FileTable =>
+          ft.fileIndex.refresh()
+          val path = new Path(ft.fileIndex.rootPaths.head.toUri)
+          val fs = path.getFileSystem(hadoopConf)
+          cacheManager.recacheByPath(session, path, fs)
+        case _ =>
+          cacheManager.recacheByPlan(session, r)
+      }
   }
 
   private def recacheTable(r: ResolvedTable, includeTimeTravel: Boolean)(): Unit = {

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Utils.scala

@@ -164,8 +164,11 @@ private[sql] object DataSourceV2Utils extends Logging {
     // `HiveFileFormat`, when running tests in sql/core.
     if (DDLUtils.isHiveTable(Some(provider))) return None
     DataSource.lookupDataSourceV2(provider, conf) match {
-      // TODO(SPARK-28396): Currently file source v2 can't work with tables.
-      case Some(p) if !p.isInstanceOf[FileDataSourceV2] => Some(p)
+      // TODO(SPARK-56175): File source V2 catalog table loading
+      // is not yet fully supported (stats, partition management,
+      // data type validation gaps).
+      case Some(_: FileDataSourceV2) => None
+      case Some(p) => Some(p)
       case _ => None
     }
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/FileTable.scala

@@ -26,7 +26,9 @@ import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.connector.catalog.{SupportsRead, SupportsWrite, Table, TableCapability}
 import org.apache.spark.sql.connector.catalog.TableCapability._
 import org.apache.spark.sql.connector.expressions.Transform
-import org.apache.spark.sql.connector.write.{LogicalWriteInfo, LogicalWriteInfoImpl}
+import org.apache.spark.sql.connector.write.{LogicalWriteInfo,
+  LogicalWriteInfoImpl, SupportsDynamicOverwrite,
+  SupportsTruncate, Write, WriteBuilder}
 import org.apache.spark.sql.errors.QueryCompilationErrors
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.streaming.runtime.MetadataLogFileIndex
@@ -49,18 +51,27 @@ abstract class FileTable(
     val caseSensitiveMap = options.asCaseSensitiveMap.asScala.toMap
     // Hadoop Configurations are case sensitive.
     val hadoopConf = sparkSession.sessionState.newHadoopConfWithOptions(caseSensitiveMap)
-    if (FileStreamSink.hasMetadata(paths, hadoopConf, sparkSession.sessionState.conf)) {
-      // We are reading from the results of a streaming query. We will load files from
-      // the metadata log instead of listing them using HDFS APIs.
+    // When userSpecifiedSchema is provided (e.g., write path via DataFrame API), the path
+    // may not exist yet. Skip streaming metadata check and file existence checks.
+    val isStreamingMetadata = userSpecifiedSchema.isEmpty &&
+      FileStreamSink.hasMetadata(paths, hadoopConf, sparkSession.sessionState.conf)
+    if (isStreamingMetadata) {
       new MetadataLogFileIndex(sparkSession, new Path(paths.head),
         options.asScala.toMap, userSpecifiedSchema)
     } else {
-      // This is a non-streaming file based datasource.
-      val rootPathsSpecified = DataSource.checkAndGlobPathIfNecessary(paths, hadoopConf,
-        checkEmptyGlobPath = true, checkFilesExist = true, enableGlobbing = globPaths)
-      val fileStatusCache = FileStatusCache.getOrCreate(sparkSession)
+      val checkFilesExist = userSpecifiedSchema.isEmpty
+      val rootPathsSpecified =
+        DataSource.checkAndGlobPathIfNecessary(
+          paths, hadoopConf,
+          checkEmptyGlobPath = checkFilesExist,
+          checkFilesExist = checkFilesExist,
+          enableGlobbing = globPaths)
+      val fileStatusCache =
+        FileStatusCache.getOrCreate(sparkSession)
       new InMemoryFileIndex(
-        sparkSession, rootPathsSpecified, caseSensitiveMap, userSpecifiedSchema, fileStatusCache)
+        sparkSession, rootPathsSpecified,
+        caseSensitiveMap, userSpecifiedSchema,
+        fileStatusCache)
     }
   }
 
@@ -174,8 +185,43 @@ abstract class FileTable(
       writeInfo.rowIdSchema(),
       writeInfo.metadataSchema())
   }
+
+  /**
+   * Creates a [[WriteBuilder]] that supports truncate and
+   * dynamic partition overwrite for file-based tables.
+   */
+  protected def createFileWriteBuilder(
+      info: LogicalWriteInfo)(
+      buildWrite: (LogicalWriteInfo, StructType,
+        Map[Map[String, String], String],
+        Boolean, Boolean) => Write
+  ): WriteBuilder = {
+    new WriteBuilder with SupportsDynamicOverwrite with SupportsTruncate {
+      private var isDynamicOverwrite = false
+      private var isTruncate = false
+
+      override def overwriteDynamicPartitions(): WriteBuilder = {
+        isDynamicOverwrite = true
+        this
+      }
+
+      override def truncate(): WriteBuilder = {
+        isTruncate = true
+        this
+      }
+
+      override def build(): Write = {
+        val merged = mergedWriteInfo(info)
+        val partSchema = fileIndex.partitionSchema
+        buildWrite(merged, partSchema,
+          Map.empty, isDynamicOverwrite, isTruncate)
+      }
+    }
+  }
+
 }
 
 object FileTable {
-  private val CAPABILITIES = util.EnumSet.of(BATCH_READ, BATCH_WRITE)
+  private val CAPABILITIES = util.EnumSet.of(
+    BATCH_READ, BATCH_WRITE, TRUNCATE, OVERWRITE_DYNAMIC)
 }